A Geographic Information System for Great Lakes Aquatic Habitat


The factors influencing variability of Great Lakes fishes occur over large spatial and temporal scales, creating challenges for protection and management of sensitive life stages and habitats. Critical habitats for spawning, feeding, and growth of many Great Lakes fishes extend from deep offshore waters to coastal or tributary habitats. Large gyres advect larvae of lake spawners into, or away from, critical nursery areas that may be less productive as a result of exotic species invasions and nutrient declines. The proximity of nearshore and tributary regions to human habitation leaves them vulnerable to a variety of anthropogenic threats, thus threatening spawning and nursery habitats of potamodromous fishes. It is essential that structure, function, and connectivity among tributary, nearshore, and offshore habitats be identified and reference conditions for habitat quality established to ensure the long-term sustainability of these important lake regions.


This project aims to develop ecological classifications of fish habitat for each of the 5 Great Lakes (Superior, Michigan, Huron, Erie, and Superior), and develop indices of relative habitat quality (growth potential, spawning and recruitment potential), harvest and movement of selected fish species within these ecoregions.


Integration of large, spatially explicit databases and ecological classification of fish habitats can provide the framework for setting reference expectations for fish health and survival across a large region. With a computerized ecological classification system, reference conditions can be estimated (modeled) for all habitats in a region based on the unique landscape position and characteristics of each site. Spatially explicit measurements of physical and biological habitats can facilitate prediction of fish production (Mason et al. 1996), impacts by exotic species (zebra mussel, ruffe) (Nalepa et al. 1995), pollution (EPA), and coastal zone development. Analysis of spatial patterns of fish distributions, abundances, and harvest, and the factors that affect those patterns will facilitate management of fisheries resources and stimulate research on the appropriate spatial and temporal scales.


Management directed towards long-term sustainability of fisheries resources should in part be based on a fundamental understanding of functional relationships between fishes and their critical habitats on the appropriate spatial and temporal scales. Regional ecological classification has been utilized as a management tool by managers of terrestrial landscapes, including streams and small lakes on the landscape.

While GIS-based ecological classifications have been developed for many terrestrial and aquatic systems in the Great Lakes basin, to date there has been little development of ecoregions directed at the Great Lakes themselves, although spatially explicit data and methods are now available. Recent advances in hydro-acoustic and remote-sensing technology (i.e. AVHRR, which estimates sea surface temperature) have encouraged collection of spatially explicit estimates of fish production, primary and secondary production, and abiotic factors (i.e. wind, currents, temperature) in the Great Lakes. Examination of these datasets, along with analysis of historic fisheries data, will facilitate understanding of patterns in distribution and abundance of important sport fishes and their prey.


Ecological classification of Great Lakes fish habitats are being developed using an iterative cycle of data analysis, map development, examination, sampling, revision, and re-examination. Classifications of ecoregions in each lake are being made using data from a variety of sources collected independently over various time periods. Data are being geo-referenced and assembled in databases in a GIS. Geostatistics are used to expand discrete point data collected during historic surveys into broader polygons. Multiple map themes are examined for spatial correspondence of key variables; this analysis provides tests of the initial delineation hypotheses. Map layers representing indicator species density and distributions will be generated.

Open-water ecoregion classifications and databases will be merged with other existing river and coastal wetland classifications and their resulting GIS layers to delineate critical spawning and nursery areas at the appropriate spatial scales. This will include mapped spatial units and a relational database of summarized attribute classes that describe a suite of physical and biological attributes for each unit. Unit attributes will include both recorded observations and predictions from models (ours and others) of likely physical and biological characteristics. Combinations of our classification with existing work on rivers and coastal wetlands and developing work on offshore regions will allow, for the first time, description of spatial relationships that influence larger-scale ecological processes at work within the lake ecosystems. GIS-based models will be used to estimate indices of fish health (bioenergetic growth potential) or spawning and recruitment potential of key species in newly defined ecoregions to provide guidelines for managers or restoration initiatives.

Preliminary Results

A report on preliminary cluster analysis results can be found here.

Shapefiles of preliminary clusters by lake can be downloaded by clicking on the links below:

Lake Superior (last updated: 10/05/2007)
Lake Michigan (last updated: 10/05/2007)
Lake Huron (last updated: 10/05/2007)
Lake Erie (last updated: 10/05/2007)
Lake Ontario (last updated: 10/05/2007)

Links to Other Efforts

The Coastal Aquatic Gap Analysis is a means of gathering and organizing existing information about spatial distributions of aquatic organisms so that it may be displayed and analyzed. Gap Analysis will enable scientists to project the occurrence of species and biotic assemblages in areas that have not been explicitly sampled, based on models of the relationships between those species and enduring environmental features. This type of analysis can be used to address many biodiversity issues, as well as species-specific management concerns.


Mason, D.M., and Brandt, S.B. 1996 Effects of spatial scale and foraging efficiency on the predictions made by spatially explicit models of fish growth rate potential. Env. Biol. Fish. 45(3):283-298.

Nalepa, T.F., Wojcik, J.A., Fanslow, D.L., and Lang, G.A., 1995. Initial colonization of the zebra mussel (Dreissena polymorpha) in Saginaw Bay, Lake Huron: population recruitment, density, and size structure. J. Great Lakes Res. 21:417-434.


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